Preset picture test error #30
Description
I test according to the pictures under your Preset folder. The following errors are in the program running results. What is the reason? Thank you for your reply.
Seed set to 233
using device cuda
ControlLDM: Running in eps-prediction mode
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
DiffusionWrapper has 865.91 M params.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or None for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing weights=VGG16_Weights.IMAGENET1K_V1. You can also use weights=VGG16_Weights.DEFAULT to get the most up-to-date weights.
warnings.warn(msg)
loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
timesteps used in spaced sampler:
[0, 23, 45, 68, 91, 114, 136, 159, 182, 204, 227, 250, 272, 295, 318, 341, 363, 386, 409, 431, 454, 477, 499, 522, 545, 568, 590, 613, 636, 658, 681, 704, 727, 749, 772, 795, 817, 840, 863, 885, 908, 931, 954, 976, 999]
Spaced Sampler: 0%| | 0/45 [00:00<?, ?it/s]WARNING:xformers:Blocksparse is not available: the current GPU does not expose Tensor cores
Traceback (most recent call last):
File "/home/aaa/Diffusion/CCSR-main/inference_ccsr.py", line 213, in
main()
File "/home/aaa/Diffusion/CCSR-main/inference_ccsr.py", line 193, in main
preds = process(
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/aaa/Diffusion/CCSR-main/inference_ccsr.py", line 69, in process
samples = sampler.sample_ccsr(
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/aaa/Diffusion/CCSR-main/model/q_sampler.py", line 1010, in sample_ccsr
"c_crossattn": [self.model.get_learned_conditioning([positive_prompt] * b)]
File "/home/aaa/Diffusion/CCSR-main/ldm/models/diffusion/ddpm_ccsr_stage2.py", line 812, in get_learned_conditioning
c = self.cond_stage_model.encode(c)
File "/home/aaa/Diffusion/CCSR-main/ldm/modules/encoders/modules.py", line 195, in encode
return self(text)
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/home/aaa/Diffusion/CCSR-main/ldm/modules/encoders/modules.py", line 172, in forward
z = self.encode_with_transformer(tokens.to(next(self.model.parameters()).device))
File "/home/aaa/Diffusion/CCSR-main/ldm/modules/encoders/modules.py", line 179, in encode_with_transformer
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
File "/home/aaa/Diffusion/CCSR-main/ldm/modules/encoders/modules.py", line 191, in text_transformer_forward
x = r(x, attn_mask=attn_mask)
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/open_clip/transformer.py", line 263, in forward
x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/open_clip/transformer.py", line 250, in attention
return self.attn(
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/nn/modules/activation.py", line 1158, in forward
merged_mask, mask_type = self.merge_masks(attn_mask, key_padding_mask, query)
File "/home/aaa/anaconda3/envs/ccsr/lib/python3.9/site-packages/torch/nn/modules/activation.py", line 1264, in merge_masks
attn_mask_expanded = attn_mask.view(1, 1, seq_len, seq_len).expand(batch_size, self.num_heads, -1, -1)
RuntimeError: shape '[1, 1, 1, 1]' is invalid for input of size 5929
Spaced Sampler: 0%| | 0/45 [00:02<?, ?it/s]